PhD Studentship in Aeronautics: Machine Learning for alternative power generation systems in aerospace applications (AE0067)

Start date: 1st December 2024 (earliest), 1st October 2025 (latest).

Supervisor: Dr Urban Fasel

Introduction: Airbus has an ambitious agenda for developing new breakthrough technologies such as hydrogen-powered commercial aircraft where a variety of aircraft configurations and propulsion technologies are being explored. Simulation models play a vital role in systematically de-risking, sizing, and forecasting the performance of these systems. The requirements of such models differ depending on the use case, for instance high fidelity models may be needed for performance predictions, while low-fidelity models may be needed for control system design.

Objectives: In this Airbus-funded PhD project, you will develop data-driven surrogate modelling techniques and explore efficient workflows to reduce the manual effort in simulation model building and improve model consistency. You will develop and extend automated model discovery that will enable cross-platform model integration and ease the use of detailed reference behaviour for applications such as controls development.

Learning opportunities: Upon completion of the projects, you will be an expert in alternative power generation systems and will have developed skills in numerical modelling, machine learning, and data-driven modelling and control. 

Duration: 3.5 years.

Funding: Full coverage of tuition fees and an annual tax-free stipend of £21,237 for Home students. These projects are within the Doctoral Programme in Sustainable Aviation Industry Partnership with Airbus. 

Eligibility: These studentships are available to students eligible for Home fees.

• Having obtained or expect to obtain a 1st class honours Master’s (or higher) degree in Aerospace Engineering or allied disciplines such as Computational Engineering, Mechanical Engineering, Mathematics, or Physics.
• Ability to develop and apply new concepts while prioritising work in response to deadlines.
• Creative approach to problem-solving.
• Ability to organise own work with minimal supervision.
• Excellent background in numerical methods, and scientific computing.
• Excellent verbal and written technical communication skills and the ability to write clearly and succinctly for publication.

How to apply: Submit your application on our Apply Webpages. You will need to include the reference AE0067 and address your application to Department of Aeronautics. When submitting your application, you will need to use the following details:

Search course/Programme: Aeronautics Research (PhD)
Research Topic: Please use reference number AE0067
Research Supervisor: Dr Urban Fasel
Research Group: Aero

Application deadline: 3 December 2024.

For further information: For questions about the project please contact Dr Urban Fasel, Lecturer in Data-Driven Aerospace Engineering, u.fasel@imperial.ac.uk. You can also learn more about Imperial at: www.imperial.ac.uk/study/pg

For queries regarding the application process, please contact Lisa Kelly: l.kelly@imperial.ac.uk

Equality, Diversity and Inclusion: Imperial is committed to equality and valuing diversity. We are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and are working in partnership with GIRES to promote respect for trans people.